In order to make the field development decision making and planning process tractable, the decision-makers usually need a few representative models (e.g., P10, P50, P90 models) selected from a large ensemble of reservoir models. This ensemble of models may have been obtained from a static and/or dynamic modeling process involving uncertainty quantification (ED), history matching, optimization, a combination thereof, or other workflows. The usual approach to select a few models is using various variants of clustering. This selection process is not only suboptimal, but it could also be quite difficult to do if multiple output responses and/or percentiles are required and the number of models is large. The current approach in most oil companies is even more naïve, wherein, such models are chosen manually or using Excel spreadsheets. Thus, due to the unavailability of good approaches, representative models are usually chosen based on one or two criteria. Use of such models in the decision making process can lead to suboptimal decisions. As such, there is a need to automatically select a small set of statistically representative models from a larger set based on multiple decision criteria.